一种用于计算机断层结肠镜检查的新型结肠壁平坦化模型:方法与验证。

Huafeng Wang, Yuexi Chen, Lihong Li, Haixia Pan, Xianfeng Gu, Zhengrong Liang
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引用次数: 0

摘要

计算机断层结肠镜(CTC)已发展为结肠癌的筛查。将三维(3D)结肠壁平坦化为二维(2D)图像被认为(1)为内镜视图提供补充信息,并进一步(2)便于结肠登记、大肠杆菌(TC)检测和口部褶皱分割。以往使用的基于保角映射的平坦化方法虽然可以保留结肠内壁的角度几何形状,但由于缺乏起伏的地形,在提供准确的三维结肠内壁信息方面存在局限性。在本文中,我们提出了一种新的使用2.5D方法的结肠壁平坦化方法。该方法结合保形平坦化模型,建立了一个高程距离图来描述结肠内壁的邻域特征。我们通过两个CTC应用验证了新方法:TC检测和haustral褶皱分割。实验结果证明了我们的策略在CTC研究中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A Novel Colon Wall Flattening Model for Computed Tomographic Colonography: Method and Validation.

Computed tomographic colonography (CTC) has been developed for screening of colon cancer. Flattening the three-dimensional (3D) colon wall into two-dimensional (2D) image is believed to (1) provide supplementary information to the endoscopic views and further (2) facilitate colon registration, taniae coli (TC) detection, and haustral fold segmentation. Though the previously-used conformal mapping-based flattening methods can preserve the angular geometry, they have the limitations in providing accurate information of the 3D inner colon wall due to the lack of undulating topography. In this paper, we present a novel colon-wall flattening method using a strategy of 2.5D approach. Coupling with the conformal flattening model, the presented new approach builds an elevation distance map to depict the neighborhood characteristics of the inner colon wall. We validated the new method via two CTC applications: TC detection and haustral fold segmentation. Experimental results demonstrated the effectiveness of our strategy for CTC studies.

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来源期刊
CiteScore
2.80
自引率
6.20%
发文量
102
期刊介绍: Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization is an international journal whose main goals are to promote solutions of excellence for both imaging and visualization of biomedical data, and establish links among researchers, clinicians, the medical technology sector and end-users. The journal provides a comprehensive forum for discussion of the current state-of-the-art in the scientific fields related to imaging and visualization, including, but not limited to: Applications of Imaging and Visualization Computational Bio- imaging and Visualization Computer Aided Diagnosis, Surgery, Therapy and Treatment Data Processing and Analysis Devices for Imaging and Visualization Grid and High Performance Computing for Imaging and Visualization Human Perception in Imaging and Visualization Image Processing and Analysis Image-based Geometric Modelling Imaging and Visualization in Biomechanics Imaging and Visualization in Biomedical Engineering Medical Clinics Medical Imaging and Visualization Multi-modal Imaging and Visualization Multiscale Imaging and Visualization Scientific Visualization Software Development for Imaging and Visualization Telemedicine Systems and Applications Virtual Reality Visual Data Mining and Knowledge Discovery.
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